
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertConfig,
		    DPRConfig,
		    TFDPRContextEncoder,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Union[str, Any]	,A_  :   List[Any]=13	,A_  :   Optional[Any]=7	,A_  :   str=True	,A_  :   List[Any]=True	,A_  :   List[str]=True	,A_  :   List[Any]=True	,A_  :   Optional[int]=99	,A_  :   int=32	,A_  :   int=2	,A_  :   Any=4	,A_  :   Any=37	,A_  :   str="gelu"	,A_  :   Tuple=0.1	,A_  :   List[str]=0.1	,A_  :   Union[str, Any]=512	,A_  :   Optional[int]=16	,A_  :   Tuple=2	,A_  :   Optional[Any]=0.02	,A_  :   int=3	,A_  :   Tuple=4	,A_  :   int=None	,A_  :   Optional[int]=0	,)  -> Optional[Any]:
							A     = parent
							A     = batch_size
							A     = seq_length
							A     = is_training
							A     = use_input_mask
							A     = use_token_type_ids
							A     = use_labels
							A     = vocab_size
							A     = hidden_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = intermediate_size
							A     = hidden_act
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = max_position_embeddings
							A     = type_vocab_size
							A     = type_sequence_label_size
							A     = initializer_range
							A     = num_labels
							A     = num_choices
							A     = scope
							A     = projection_dim


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Tuple:
							A     = ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size   )

							A     = None
							if self.use_input_mask:
								# follow test_modeling_tf_ctrl.py
								A     = random_attention_mask([self.batch_size, self.seq_length]   )

							A     = None
							if self.use_token_type_ids:
								A     = ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size   )

							A     = None
							A     = None
							A     = None
							if self.use_labels:
								A     = ids_tensor([self.batch_size]	,self.type_sequence_label_size   )
								A     = ids_tensor([self.batch_size, self.seq_length]	,self.num_labels   )
								A     = ids_tensor([self.batch_size]	,self.num_choices   )

							A     = BertConfig(
							    vocab_size=self.vocab_size	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,max_position_embeddings=self.max_position_embeddings	,type_vocab_size=self.type_vocab_size	,is_decoder=A_	,initializer_range=self.initializer_range	,)
							A     = DPRConfig(projection_dim=self.projection_dim	,**config.to_dict()   )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   str	,A_  :   Tuple	,A_  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]	,A_  :   Tuple	,A_  :   List[str]   )  -> List[Any]:
							A     = TFDPRContextEncoder(config=A_   )
							A     = model(A_	,attention_mask=A_	,token_type_ids=A_   )
							A     = model(A_	,token_type_ids=A_   )
							A     = model(A_   )
							self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.projection_dim or self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   List[Any]	,A_  :   Optional[Any]	,A_  :   Tuple	,A_  :   Union[str, Any]	,A_  :   Union[str, Any]	,A_  :   Optional[Any]	,A_  :   Tuple   )  -> List[str]:
							A     = TFDPRQuestionEncoder(config=A_   )
							A     = model(A_	,attention_mask=A_	,token_type_ids=A_   )
							A     = model(A_	,token_type_ids=A_   )
							A     = model(A_   )
							self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.projection_dim or self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Optional[Any]	,A_  :   str	,A_  :   str	,A_  :   Optional[int]	,A_  :   str	,A_  :   str	,A_  :   List[Any]   )  -> List[str]:
							A     = TFDPRReader(config=A_   )
							A     = model(A_	,attention_mask=A_   )

							self.parent.assertEqual(result.start_logits.shape	,(self.batch_size, self.seq_length)   )
							self.parent.assertEqual(result.end_logits.shape	,(self.batch_size, self.seq_length)   )
							self.parent.assertEqual(result.relevance_logits.shape	,(self.batch_size,)   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Union[str, Any]:
							A     = self.prepare_config_and_inputs()
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = config_and_inputs
							A     = {'input_ids': input_ids}
							return config, inputs_dict







@require_tf
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Any   =     (
						    (
						        TFDPRContextEncoder,
						        TFDPRQuestionEncoder,
						        TFDPRReader,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:     Any   =     {'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}

						_lowerCamelCase:     Tuple   =     False
						_lowerCamelCase:     str   =     False
						_lowerCamelCase:     int   =     False
						_lowerCamelCase:     List[Any]   =     False
						_lowerCamelCase:     Union[str, Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Dict:
							A     = TFDPRModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,hidden_size=37   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Any:
							self.config_tester.run_common_tests()


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[int]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> int:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = TFDPRContextEncoder.from_pretrained(A_   )
								self.assertIsNotNone(A_   )

							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = TFDPRContextEncoder.from_pretrained(A_   )
								self.assertIsNotNone(A_   )

							for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = TFDPRQuestionEncoder.from_pretrained(A_   )
								self.assertIsNotNone(A_   )

							for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = TFDPRReader.from_pretrained(A_   )
								self.assertIsNotNone(A_   )







@require_tf
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Any:
							A     = TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'   )

							A     = tf.constant(
							    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]   )  # [CLS] hello, is my dog cute? [SEP]
							A     = model(A_   )[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							A     = tf.constant(
							    [
							        [
							            0.03_23_62_53,
							            0.12_75_33_35,
							            0.16_81_85_09,
							            0.00_27_97_86,
							            0.3_89_69_33,
							            0.24_26_49_45,
							            0.2_17_89_71,
							            -0.02_33_52_27,
							            -0.08_48_19_59,
							            -0.14_32_41_17,
							        ]
							    ]   )
							self.assertTrue(numpy.allclose(output[:, :10].numpy()	,expected_slice.numpy()	,atol=1e-4   )   )



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()

         snake_case											=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         snake_case											=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES['''vocab_file''']       )
         with open(self.vocab_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

         snake_case											=							{
             '''do_resize''': True,
             '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
             '''do_normalize''': True,
             '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
             '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
             '''do_convert_rgb''': True,
         }
         snake_case											=							os.path.join(self.tmpdirname     ,   __snake_case       )
         with open(self.image_processor_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as fp:
              json.dump(__snake_case     ,   __snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizer.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							[np.random.randint(2_5_5     ,   size=(3, 3_0, 4_0_0)     ,   dtype=np.uinta       )]

         snake_case											=							[Image.fromarray(np.moveaxis(__snake_case     ,   0     ,   -1       )       ) for x in image_inputs]

         return image_inputs
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_rust_tokenizer()
         snake_case											=							self.get_image_processor()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_slow.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,   use_fast=__snake_case       )

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_fast.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()     ,   tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer     ,   __snake_case       )
         self.assertIsInstance(processor_fast.tokenizer     ,   __snake_case       )

         self.assertEqual(processor_slow.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor     ,   __snake_case       )
         self.assertIsInstance(processor_fast.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''       )
         snake_case											=							self.get_image_processor(do_normalize=__snake_case       )

         snake_case											=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname     ,   cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''     ,   do_normalize=__snake_case       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							self.prepare_image_inputs()

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   processor.model_input_names       )




